tensorflow / transform
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

Intro
  • Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
  • Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
  • Conditional complexity is measured at the unit level (methods, functions...).
  • Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
Learn more...
Conditional Complexity Overall
  • There are 846 units with 9,355 lines of code in units (76.9% of code).
    • 0 very complex units (0 lines of code)
    • 3 complex units (274 lines of code)
    • 31 medium complex units (1,327 lines of code)
    • 87 simple units (2,232 lines of code)
    • 725 very simple units (5,522 lines of code)
0% | 2% | 14% | 23% | 59%
Legend:
51+
26-50
11-25
6-10
1-5
Alternative Visuals
Conditional Complexity per Extension
51+
26-50
11-25
6-10
1-5
py0% | 2% | 14% | 23% | 59%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
tensorflow_transform0% | 3% | 14% | 22% | 60%
tensorflow_transform/saved0% | 26% | 0% | 19% | 53%
tensorflow_transform/beam0% | 0% | 13% | 28% | 58%
tensorflow_transform/tf_metadata0% | 0% | 35% | 13% | 50%
tensorflow_transform/coders0% | 0% | 33% | 22% | 43%
tensorflow_transform/experimental0% | 0% | 0% | 35% | 64%
tensorflow_transform/py_func0% | 0% | 0% | 53% | 46%
ROOT0% | 0% | 0% | 34% | 65%
tensorflow_transform/beam/tft_beam_io0% | 0% | 0% | 0% | 100%
tensorflow_transform/beam/experimental0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def compute_tukey_hh_params()
in tensorflow_transform/gaussianization.py
 71 28 1
def _partially_apply_saved_transform_impl()
in tensorflow_transform/saved/saved_transform_io.py
 114 26 3
def vocabulary()
in tensorflow_transform/analyzers.py
 89 26 16
def _make_cast_fn()
in tensorflow_transform/coders/example_proto_coder.py
 35 22 1
def schema_as_feature_spec()
in tensorflow_transform/tf_metadata/schema_utils.py
 43 18 1
def _infer_feature_schema_common()
in tensorflow_transform/schema_inference.py
 46 17 9
def _compute_analysis_results_for_func_attributes()
in tensorflow_transform/graph_tools.py
 52 16 3
def apply_saved_model()
in tensorflow_transform/pretrained_models.py
 75 16 5
def apply_vocabulary()
in tensorflow_transform/mappers.py
 65 15 10
def _ApplyThresholdsAndTopK()
in tensorflow_transform/beam/analyzer_impls.py
 49 15 5
def _get_items_to_clone()
in tensorflow_transform/beam/deep_copy.py
 30 15 1
def _validate_and_get_dense_value_key_inputs()
in tensorflow_transform/tf_utils.py
 38 14 2
def _calculate_mutual_information_for_feature_value()
in tensorflow_transform/beam/analyzer_impls.py
 51 14 4
def _visit_partitionable_operation()
in tensorflow_transform/beam/analysis_graph_builder.py
 25 14 3
def _scale_to_z_score_internal()
in tensorflow_transform/mappers.py
 46 13 5
def ngrams()
in tensorflow_transform/mappers.py
 49 13 5
def _numeric_combine()
in tensorflow_transform/analyzers.py
 31 13 9
def __init__()
in tensorflow_transform/coders/csv_coder.py
 60 13 7
def analyze_tensor()
in tensorflow_transform/graph_tools.py
 26 12 2
def _assign_buckets()
in tensorflow_transform/mappers.py
 17 12 2